




'''simple docstring'''


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = False   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     List[str]:
     super().__init__(
         features=UpperCamelCase   ,				cache_dir=UpperCamelCase   ,				keep_in_memory=UpperCamelCase   ,				streaming=UpperCamelCase   ,				num_proc=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       Generator(
         cache_dir=UpperCamelCase   ,				features=UpperCamelCase   ,				generator=UpperCamelCase   ,				gen_kwargs=UpperCamelCase   ,				**UpperCamelCase   ,				)







   def 		UpperCamelCase__      (						self   )					->     str:
     # Build iterable dataset
     if self.streaming:
       __a								=       self.builder.as_streaming_dataset(split='train'   )
     # Build regular (map-style) dataset
     else:
       __a								=       None
       __a								=       None
       __a								=       None
       __a								=       None

       self.builder.download_and_prepare(
           download_config=UpperCamelCase   ,				download_mode=UpperCamelCase   ,				verification_mode=UpperCamelCase   ,				base_path=UpperCamelCase   ,				num_proc=self.num_proc   ,				)
       __a								=       self.builder.as_dataset(
           split='train'   ,				verification_mode=UpperCamelCase   ,				in_memory=self.keep_in_memory   )
     return dataset









'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'					,       [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Dict      ):
  __a								=       tmp_path_factory.mktemp('dset_infos_dir'      )
  if "full:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / 'dataset_infos.json'					,       'w'      ) as f:
      f.write('{"default": {"dataset_size": 42}}'      )
  __a								=       DatasetInfosDict.from_directory(a_      )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'					,       [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				DatasetInfo      ):
  __a								=       str(a_      )
  dataset_info.write_to_directory(a_      )
  __a								=       DatasetInfo.from_directory(a_      )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(a_					,       'dataset_info.json'      )      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo(
      description='foo'					,       citation='bar'					,       homepage='https://foo.bar'					,       license='CC0'					,       features=Features({'a': Value('int32'      )}      )					,       post_processed={}					,       supervised_keys=()					,       task_templates=[]					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train', 'num_examples': 42}]					,       download_checksums={}					,       download_size=1337					,       post_processing_size=442					,       dataset_size=1234					,       size_in_bytes=1337 + 442 + 1234					,       )
  __a								=       dataset_info._to_yaml_dict()
  assert sorted(a_      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]					,       (list, dict, int, str)      )
  __a								=       yaml.safe_dump(a_      )
  __a								=       yaml.safe_load(a_      )
  assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo()
  __a								=       dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'					,       [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       )
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				DatasetInfosDict      ):
  __a								=       str(a_      )
  dataset_infos_dict.write_to_directory(a_      )
  __a								=       DatasetInfosDict.from_directory(a_      )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __a								=       config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __a								=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(a_					,       'README.md'      )      )



